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There's this continual refrain from AI people: "well evolution is just brute force too", or "the brain is just doing complex statistics too" as if that explains

There's this continual refrain from AI people: "well evolution is just brute force too", or "the brain is just doing complex statistics too" as if that explains anything at all. There are different ways of implementing "brute force" and "complex statistics" - what matters is the implementation, not the terminology. Clearly AI is not even remotely close to "thinking" or "awareness" in the human sense. We don't even understand the implementation of those things in humans. It is ignorant conceit to think we will just magically stumble upon the answer with zeitgeist machine learning techniques.

mindcrime 8y ago
We don't even understand the implementation of those things in humans. And we don't have to, unless you're insisting on something that might be referred to more aptly as "Artificial Human Intelligence". Generally speaking though, in the AI field, there's no particular belief that AI must work the same way human intelligence works. If AI research leads to discoveries that help us better understand human intelligence, that's a nice perk, but nobody treats that as the ultimate goal. That said, of course it makes sense to try and model after human intelligence to the extent we can, since we are currently the best example of intelligence we have available to use as a template. It is ignorant conceit to think we will just magically stumble upon the answer with zeitgeist machine learning techniques. Who is out there suggesting that we will "just magically stumble upon the answer with zeitgeist machine learning techniques"? From where I sit, it seems that most contemporary researchers who are focused on Deep Learning / Deep Reinforcement Learning / etc. are not talking much at all about "Artificial Intelligence" in the general sense. And the people out there talking specifically about "Artificial General Intelligence" (Ben Goertzel, Marcus Hutter, Pei Wang, etc.) certainly aren't claiming that all we need is the currently faddish ML techniques. See, for reference: http://agi-conf.org/2017/?page_id=20 http://agi-conf.org/2017/?page_id=20 http://agi-conf.org/2016/schedule/ http://agi-conf.org/2016/schedule/ http://agi-conf.org/2015/schedule/ http://agi-conf.org/2015/schedule/ etc..
- jokoon 8y ago
  > but nobody treats that as the ultimate goal. I remember watching Andrew Ng's course on ML and he was often talking about "AI dream". I think that the goal of AI is to build machines that are progressively more intelligent. To build those machine you have to build an artificial form of intelligence, to further analyze and research what intelligence really means. I don't think humans are really able to visualize a form of intelligence other than human or mammal/earthly life forms. Our intelligence comes from an evolutionary need to figure out things in order to survive, but it's an earthly version of how we evolved. With that said, I think we can already say that our definition of intelligence will always biased because we put our human intelligence on a pedestal, and worst than that, we won't be able to detect other forms of intelligence because of those reasons.

mindcrime 8y ago

We don't even understand the implementation of those things in humans. And we don't have to, unless you're insisting on something that might be referred to more aptly as "Artificial Human Intelligence". Generally speaking though, in the AI field, there's no particular belief that AI must work the same way human intelligence works. If AI research leads to discoveries that help us better understand human intelligence, that's a nice perk, but nobody treats that as the ultimate goal. That said, of course it makes sense to try and model after human intelligence to the extent we can, since we are currently the best example of intelligence we have available to use as a template. It is ignorant conceit to think we will just magically stumble upon the answer with zeitgeist machine learning techniques. Who is out there suggesting that we will "just magically stumble upon the answer with zeitgeist machine learning techniques"? From where I sit, it seems that most contemporary researchers who are focused on Deep Learning / Deep Reinforcement Learning / etc. are not talking much at all about "Artificial Intelligence" in the general sense. And the people out there talking specifically about "Artificial General Intelligence" (Ben Goertzel, Marcus Hutter, Pei Wang, etc.) certainly aren't claiming that all we need is the currently faddish ML techniques. See, for reference: http://agi-conf.org/2017/?page_id=20 http://agi-conf.org/2017/?page_id=20 http://agi-conf.org/2016/schedule/ http://agi-conf.org/2016/schedule/ http://agi-conf.org/2015/schedule/ http://agi-conf.org/2015/schedule/ etc..

jokoon 8y ago
> but nobody treats that as the ultimate goal. I remember watching Andrew Ng's course on ML and he was often talking about "AI dream". I think that the goal of AI is to build machines that are progressively more intelligent. To build those machine you have to build an artificial form of intelligence, to further analyze and research what intelligence really means. I don't think humans are really able to visualize a form of intelligence other than human or mammal/earthly life forms. Our intelligence comes from an evolutionary need to figure out things in order to survive, but it's an earthly version of how we evolved. With that said, I think we can already say that our definition of intelligence will always biased because we put our human intelligence on a pedestal, and worst than that, we won't be able to detect other forms of intelligence because of those reasons.